There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

On robust estimation of the location parameter

Appropriate subcellular localization is crucial for regulating p53 function. We show that p53 export is mediated by a highly conserved leucine-rich nuclear export signal (NES) located in its tetramerization domain. Mutation of NES residues prevented p53 export and hampered tetramer formation. Although the p53-binding protein MDM2 has an NES and has been proposed to mediate p53 export, we show that the intrinsic p53 NES is both necessary and sufficient for export. This report also demonstrates that the cytoplasmic localization of p53 in neuroblastoma cells is due to its hyperactive nuclear export: p53 in these cells can be trapped in the nucleus by the export-inhibiting drug leptomycin B or by binding a p53-tetramerization domain peptide that masks the NES. We propose a model in which regulated p53 tetramerization occludes its NES, thereby ensuring nuclear retention of the DNA-binding form. We suggest that attenuation of p53 function involves the conversion of tetramers into monomers or dimers, in which the NES is exposed to the proteins which mediate their export to the cytoplasm.

/pdf/a-leucine-rich-nuclear-export-signal-in-the-p53-2g2khfm670.pdf

A leucine-rich nuclear export signal in the p53 tetramerization domain: regulation of subcellular localization and p53 activity by NES masking.

Diagnostic testing to identify persons infected with severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection is central to control the global pandemic of COVID-19 that began in late 2019. In a few countries, the use of diagnostic testing on a massive scale has been a cornerstone of successful containment strategies. In contrast, the United States, hampered by limited testing capacity, has prioritized testing for specific groups of persons. Real-time reverse transcriptase polymerase chain reaction-based assays performed in a laboratory on respiratory specimens are the reference standard for COVID-19 diagnostics. However, point-of-care technologies and serologic immunoassays are rapidly emerging. Although excellent tools exist for the diagnosis of symptomatic patients in well-equipped laboratories, important gaps remain in screening asymptomatic persons in the incubation phase, as well as in the accurate determination of live viral shedding during convalescence to inform decisions to end isolation. Many affluent countries have encountered challenges in test delivery and specimen collection that have inhibited rapid increases in testing capacity. These challenges may be even greater in low-resource settings. Urgent clinical and public health needs currently drive an unprecedented global effort to increase testing capacity for SARS-CoV-2 infection. Here, the authors review the current array of tests for SARS-CoV-2, highlight gaps in current diagnostic capacity, and propose potential solutions.

/pdf/diagnostic-testing-for-severe-acute-respiratory-syndrome-1n1tj9sbn1.pdf

Diagnostic Testing for Severe Acute Respiratory Syndrome-Related Coronavirus 2: A Narrative Review.

Nuclear-cytoplasmic transport, which occurs through special structures called nuclear pores, is an important aspect of normal cell function, and defects in this process have been detected in many different types of cancer cells. These defects can occur in the signal-transduction pathways that regulate the transfer of factors such as p53 and β-catenin in and out of the nucleus, or in the general nuclear import and export machinery itself. In some cases, nuclear transport factors are overproduced, whereas in others, chromosomal translocations disrupt the structural proteins that make up the nuclear pore, leading to cell transformation. How does disruption of nuclear-cytoplasmic transport promote transformation, and is this process a viable therapeutic target?

Nuclear transport and cancer: from mechanism to intervention.

Cooperation of a single lysine mutation and a C-terminal domain in the cytoplasmic sequestration of the p53 protein.

Asymptotic performance of PCA for high-dimensional heteroscedastic data.

Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized...

Generalized Canonical Polyadic Tensor Decomposition

Tensor decomposition is a well-known tool for multiway data analysis. This work proposes using stochastic gradients for efficient generalized canonical polyadic (GCP) tensor decomposition of large-...

https://epubs.siam.org/doi/pdf/10.1137/19M1266265

Stochastic Gradients for Large-Scale Tensor Decomposition

It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs. Despite significant progress in the area, foundational open problems remain. In this paper, we address several key questions. We derive exact and approximate Bayes-optimal robust classifiers for the important setting of two- and three-class Gaussian classification problems with arbitrary imbalance, for $\ell_2$ and $\ell_\infty$ adversaries. In contrast to classical Bayes-optimal classifiers, determining the optimal decisions here cannot be made pointwise and new theoretical approaches are needed. We develop and leverage new tools, including recent breakthroughs from probability theory on robust isoperimetry, which, to our knowledge, have not yet been used in the area. Our results reveal fundamental tradeoffs between standard and robust accuracy that grow when data is imbalanced. We also show further results, including an analysis of classification calibration for convex losses in certain models, and finite sample rates for the robust risk.

David Hong

Papers

Cooperation of a single lysine mutation and a C-terminal domain in the cytoplasmic sequestration of the p53 protein.

Asymptotic performance of PCA for high-dimensional heteroscedastic data.

Generalized Canonical Polyadic Tensor Decomposition

Stochastic Gradients for Large-Scale Tensor Decomposition

Provable tradeoffs in adversarially robust classification